Strategic Innovation and Artificial Intelligence - Purple Edition

eMBA

Last Updated: 2024-09-04

Content

Goal

We start answering questions about what is innovation, how we can make our organisation the learning and innovative organsiation that we want to work for. We explore the history of innovation and use that knowledge to look into the future.
The data-science part of the course is based on "The big R-book: from data science to learning machines and big data. The homepage of the book is here.

Calendar

# Date Time Place Content
1 2023-11-17 9:00 – 12:30 classroom introduction and agreeements
2 2023-12-07 9:00 – 12:30 classroom Innovation theory, practice, business plans and MCDA
3 2024-01-18 13:30 – 17:00 classroom Using data and building regression models
4 2024-01-19 13:30 – 17:00 classroom Artificial intelligence and its use in banking
5 2024-02-02 13:30 – 17:00 classroom Ethics and bias, ethical use of data and artificial intelligence
6 2024-04-18 13:30 – 17:00 classroom Quantum computers and their potential use in banking
7 2024-04-19 9:00 – 12:30 classroom Innovation, R&D, Blockchain

Lectures and Content

# Lecture Description Downloads Other Resources
1 Introduction to the course An introduction to your teacher, layout of the course, learning goals, agreements, etc.
2 The history of innovation A historical view of banking and capitalism, the importance of exponential growth, innovations, and the great waves of capitalism. We explore the different waves and conclude that the latest wave is based on artificial intelligence, while some other promising technologies such as quantum computing, biotech and nanotech are just around the corner.
This is in line with the introduction of the book "The big R-book: from data science to learning machines and big data." The homepage of the book is here
3 Multi Criteria Decision Analytics Multi Criteria Decision Analytics is the art of making an informed choice in a situation where multiple competing criteria make it impossible to find one best soluiton (for example good quality comes at a high price - and we want a low price and high quality).This material draws on part V of the book "The big R-book: from data science to learning machines and big data." The homepage of the book is here.
4 Building Models in R This is where the rubber hits the road: the long preprations of importing data and preparing it for models comes to fruition now: we can start building models. We look into linear regressions, generalised linear regressions (eg. logistic regressions) and also machine learning techniques such as decision tree, random forest, support vector machines, neural networks, culstering with k-means, etc.This material corresponds to part V of the book "The big R-book: from data science to learning machines and big data." The homepage of the book is here.
5 Introduction to Large Language Models An overview, tailored for managers, of how large language models actually work and what one can expect from them.
6 Applications of AI in banking A deep dive about artificial intelligence and how it can be used in banking, including a practical look at pitfalls, methods and company culture.
7 Introduction to Companies To be effective in a private enterprise it is useful to understand the basics of wealth creation and how that is reflecting in a balance sheet, profit and loss statement. This value creation chain leads to wealth creation in companies and hence this is a good hook to talk about company vaulation. Company valuation is an entry to financial markets with many financial instruments such as bonds, equities, options, futures, etc.This material corresponds to part VI of the book "The big R-book: from data science to learning machines and big data." The homepage of the book is here.
8 Bias Exploring bias in our perception, reasoning, and decission making processes
9 Ethics An introduction to Ethics. What is it? What is ethical and what not? How does the refernce point of view our judgement?See references in the slides
10 Bias in data Recognising bias in data and models and building robust, unbiased models.
11 Introduction to Quantum Computing An accessible introduction to Quantum Computing.
12 Applications of Quantum Computing in Banking Preparing for the quantum era from the banker's point of view.
13 Introduction to Bitcoin and Blockchain Understanding what the hype is all about.
14 Introduction to Big Data Understanding what Big Data is and how it is essential in today's world.
15 Introduction to Crowd Funding Understanding the basics of crowd funding and how to use it to your advantage.

Exam

Students form groups of 3 to 5 people and present a groupwork. The groupworks consists of

  1. find a problem to be solved with a model (eg. build an acceptance modeld for car insurance)
  2. find an appropriate dataset
  3. build the best possible models and compare them
  4. prepare a report about the work
  5. present the work in a short presentation